Fluid Dynamics
Invariant Guided PINN for Fluid Flow Computation
Zheng Lu, Jiwei Jia, Bora Aniruddha et al. (+2 more)
Physics-informed neural networks (PINNs) often become difficult to optimize for incompressible flow problems with large spatial domains, multiscale stresses, or long-time invariant dynamics. We propose an invariant-guided PINN (IG-PINN) framework that uses partitioned training as a conservative preconditioning stage rather than as the final piecewise representation. A globally defined architecture is trained successively on spatial subdomains or temporal slabs; selected field traces, structural information, and conservative diagnostics are then transferred to a final global correction, yielding a single neural field on the full spatial or space-time domain. The framework is tested on two incompressible flow problems: steady Oldroyd–B flow past a confined cylinder and a rotational Newtonian flow with helicity diagnostics. In the Oldroyd–B case, IG-PINN transfers velocity, polymeric stress, and mass-flux information while avoiding pressure traces at artificial interfaces. In the helicity case, endpoint velocity is transferred through a hard temporal constraint and kinetic energy is controlled during slab training and residual global correction. The experiments demonstrate improved optimization robustness, reduced conservation errors for the cylinder wake, and controlled energy and helicity diagnostics for the transient rotational flow.
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All authors Zheng Lu, Jiwei Jia, Bora Aniruddha, Xingyu An, Young Ju Lee
Computational Physics · Cross-listed from cond-mat.mtrl-sci
Differentiable hybrid force fields support scalable autonomous electrolyte discovery
Xintian Wang, Junmin Chen, Zhuoying Zhu et al. (+1 more)
Autonomous electrolyte discovery demands a computational engine that satisfies a critical trilemma: it must be fast enough for high-throughput screening, accurate enough for quantitative property prediction, and calibratable enough for online refinement. Classical empirical force fields (FFs) are fast but rely on error cancellation, while standard machine learning interatomic potentials (MLIPs) are computationally expensive. In this Perspective, we highlight that differentiable hybrid FFs resolve this trilemma by fusing physically motivated functional forms with neural-network short-range corrections. Grounded in Energy Decomposition Analysis (EDA), state-of-the-art models such as PhyNEO-Electrolyte and ByteFF-Pol achieve zero-shot generalization to bulk phases, delivering throughputs on the order of tens of ns/day (up to $\sim$50 ns/day, depending on model complexity) for 10,000-atom systems. Crucially, their physical skeletons provide a well-conditioned parameter space for differentiable molecular dynamics (dMD). This enables a dual-calibration paradigm: bottom-up ab initio parameterization combined with top-down fine-tuning from macroscopic experimental observables. We propose that this architecture meets the requirements of a “ChemRobot-ready” digital twin by integrating physics-grounded simulation with experimentally calibratable refinement, thereby enabling closed-loop autonomous electrolyte discovery.
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All authors Xintian Wang, Junmin Chen, Zhuoying Zhu, Peichen Zhong
Optics
Supercell-size scaling of moiré band flatness
Peilong Hong, Yuge Qiu, Wenjing Li et al. (+9 more)
In moiré superlattices, the band flatness governs the degree of wave localization, which is central to harnessing emergent phenomena and designing functional meta-devices. While research has focused on the magic conditions such as magic angle and magic distance for optimal flatness, a fundamental understanding of how flatness changes with the supercell size has remained elusive. Here, we establish a universal scaling between band flatness and supercell size. Theoretically, by recognizing the statistical equivalence between structural perturbations in moiré superlattices and disordered systems, we introduce the Thouless number to evaluate the strength of moiré localization. This approach allows us to establish a scaling theory for the
evolution of band flatness with the supercell size, from which an analytical expression is derived. Our full-wave simulations with one-dimensional and two-dimensional moiré superlattices show excellent agreement with the theoretical prediction. Our work reveals a general scaling law for moiré band flatness, offering a new perspective for understanding and designing moiré-based resonant systems.
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All authors Peilong Hong, Yuge Qiu, Wenjing Li, Yiyin Peng, Yu Wang, Liwei Zhang, Mingfang Yi, Yuandi He, Peng Cheng, Wangping Cheng, Yi Liang, Guoquan Zhang
Fluid Dynamics
Tuning Cross-stream Lift in Viscoelastic Shear: Distinct Hydrodynamic Signatures of Force-bearing and Force-free Mechanisms
Soumyodeep Chowdhury, Kushagra Tiwari, Jitendra Dhakar et al. (+1 more)
We investigate the lift and drag corrections acting on a particle suspended in a planar viscoelastic shear flow when the particle is tuned to translate relative to the flow by an external mechanism. A cross-stream lift force arises when particle is driven in streamwise direction; we find that the nature of the driving mechanism dictates the lift direction: force-bearing mechanisms (such as
gravity acting on non-neutrally buoyant particles) and force-free mechanisms (such as electrophoresis) generate lift forces of opposite sign. By explicitly deriving the first-order fields and stresses, we demonstrate that this reversal originates from distinct hydrodynamic disturbances induced by each mechanism, which produce qualitatively different polymeric stress distributions. This analytical result is further verified through an independent derivation using the reciprocal
theorem. Further, we find that driving the particle in the gradient direction gives rise to a streamwise drag correction that is of the same sign for both mechanisms. Beyond microfluidic particle manipulation, these results have broader implications for understanding the locomotion of microswimmers in viscoelastic shear flows, where distinct force-free propulsion mechanisms are expected to generate unique force and torque modifications.
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All authors Soumyodeep Chowdhury, Kushagra Tiwari, Jitendra Dhakar, Akash Choudhary
Applied Physics · Cross-listed from quant-ph
Quantum Thermal Field Effect Transistor
Abhijeet Kumar, Soniya Malik and P. Arumugam
We propose and analyse a
quantum thermal field-effect transistor (qtFET) composed of left-
qubit, middle-qutrit, and right-
qubit subsystems. In this architecture, the left
qubit is coupled to the middle qutrit, which in turn interacts with the right
qubit. Each subsystem interacts independently with its respective baths. The middle subsystem serves as a modulator. We have shown that the qtFET exhibits functionality analogous to that of a conventional electronic field-effect transistor (eFET). The left, right, and middle subsystems of the qtFET correspond to the drain, source, and gate of an eFET in a common gate configuration, respectively. Our results show that the qtFET can precisely modulate thermal currents, highlighting its potential as a fundamental building block for
quantum thermal devices and amplifiers in emerging
quantum technologies.
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Atmospheric and Oceanic Physics
Comparing Ocean Forecasts Driven with Machine Learning-based and Physics-based Atmospheric Forcings
Xiaobing Zhou, Frank Colberg, Debra Hudson et al. (+4 more)
Operational ocean
forecasting systems conventionally employ dynamical ocean models driven by atmospheric forcing derived from numerical weather prediction (NWP) models. Recent advancements in artificial intelligence and machine learning (ML) have led to the development of ML-based atmospheric weather models, which have competitive, if not better, medium range forecast accuracy compared to traditional NWP systems. This study evaluates the impact of ML-based atmospheric forcing on ocean forecast skill through two sets of 10-day forecasts using the UK Met Office GOSI9 configuration of the NEMO dynamical ocean model. Both experiments share identical ocean initial conditions; but differ in atmospheric forcing: one uses ECMWF's ML-based AIFS model, while the other uses the Australian Bureau of Meteorology's physics-based NWP model, ACCESS-G3. Forecasts were initialized on the first day of each month over the period 2023-2024. The quality of the atmospheric forcing was assessed by comparing AIFS and ACCESS-G3 forecast skill against both ECMWF reanalysis v5 (ERA5) and ACCESS-G3 analyses. Results indicate that AIFS consistently outperforms ACCESS-G3, either from the initial forecast time or after the first few days. Oceanic forecast skill was evaluated against both the GOSI9 reanalysis and observations, focusing on key surface variables including sea surface temperature, salinity, sea level, and ocean currents. The ocean forecasts forced with AIFS atmospheric data exhibit comparable or enhanced predictive skill compared to those forced with ACCESS-G3 data. These findings underscore the potential of ML-based atmospheric models to replace traditional NWP forcing in operational ocean
forecasting systems, offering improved accuracy and computational efficiency.
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All authors Xiaobing Zhou, Frank Colberg, Debra Hudson, Yonghong Yin, Griffith Young, Christopher Bladwell, Catherine Deburgh-Day
Optics
The hidden dimension in nanophotonics design: understanding
P. Lalanne and O. Miller
Space, time, and additional dimensions spawn remarkable complexity in optics. We encourage pairing black-box simulation and design tools with a complementary tool: understanding.
Fluid Dynamics
Viscoelastic Droplet Impact on Surfaces with Sharp Wettability Contrast: Coupled Influence of Relaxation Time and Surface Tension
Mahmood Mousavi, Parisa Tayerani, Sebastian Stephens et al. (+2 more)
The impact dynamics of viscoelastic droplets on solid surfaces play a critical role in numerous applications, including inkjet printing, spray coating, and microfluidics, where precise control of spreading, retraction, and rebound is essential. This numerical study investigates the coupled influence of fluid viscoelasticity, modeled via the Oldroyd-B constitutive equation, and gravitational-capillary balance on droplet behavior upon impact onto surfaces featuring sharp hybrid wettability. Employing a high-fidelity three-dimensional OpenFOAM-based solver that integrates the volume-of-fluid method, log-conformation formulation for improved numerical stability, and a velocity-dependent dynamic contact angle model, we simulated a 2 cm-diameter droplet impacting at 4 m/s across a range of relaxation times and surface tensions. Results demonstrate that increasing the relaxation time from 0.02 s to 0.12 s enhances elastic energy storage, leading to up to 12.9% larger maximum spreading diameters (from 24.97 mm to 28.09-28.17 mm) and a 16.6% reduction in minimum droplet height across uniform and hybrid surfaces. In contrast, increasing surface tension from 0.05 N/m to 0.15 N/m suppresses maximum spreading by about 1.1% (from 27.21 mm to 26.90 mm) while increasing minimum height by 3.3% (from 2.12 mm to 2.20 mm). On hybrid surfaces with static contact angles of 0{\deg} and 160{\deg}, the sharp wettability contrast induces pronounced asymmetric spreading and directional fluid migration toward the hydrophilic region, ultimately producing distinctive dustpan- and shoe-like
equilibrium morphologies. Variations in surface tension, which simultaneously modulate the Weber and Eötvös numbers, reveal that stronger capillary forces suppress radial expansion while enhancing
curvature-driven
recoil and redistributing viscoelastic stresses.
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All authors Mahmood Mousavi, Parisa Tayerani, Sebastian Stephens, Cadence Ruskowski, Bok Jik Lee
Instrumentation and Detectors · Cross-listed from cond-mat.mtrl-sci
Alkaline-Earth Rare-Earth Fluoride Nanoparticle Superlattices for Ultrafast, Radiation Stable Scintillators
Parivash Moradifar, Tim Brandt van Driel, Masashi Fukuhara et al. (+9 more)
Radioluminescent nanostructures provide a pathway to the fabrication of next-generation scintillators with tunability in composition, size, and morphology, and spectral and temporal properties, as well as scalable processing. Here we create a 3D millimeter-scale solid-state scintillators from SrLuF Ce3+, Pr3+ (SrLuF) core-shell nanostructures, integrating nanoscale building blocks into self-assembled macroscopic
crystals. These scintillators exhibit single-digit nanosecond decay times, linear response, resistance to radiation-induced degradation, and optical emission yields within an order of magnitude of YAG Ce3+. We select a SrLuF host
lattice owing to its high effective atomic number, wide band gap, and low
phonon energy, which together support efficient 4f-5d radiative transitions from Ce3+ and Pr3+ activators while suppressing afterglow. We create a library of core-shell nanoscintillators with undoped SrLuF shells and cores spanning compositions from undoped SrLuF to fully doped SrCeF or SrPrF. Time-resolved and steady-state X-ray excited optical luminescence (XEOL) reveal broadband emission at 310 nm (Ce3+) and 335 nm (Pr3+) with biexponential decays in the sub-nanosecond (100-500 ps) and sub-15 ns (4-13 ns) regimes, demonstrating tunable radiative efficiency and ultrafast dynamics. Ensemble performance of the mm-scale superlattices is characterized under both continuous-wave and femtosecond high-intensity excitation, revealing high light yield, linear response, and radiation hardness under extreme irradiation of ultrafast 50fs X-ray pulses up to 5mJ per mm2 corresponding to a peak intensity of 1013 W per cm2. Together, these results establish a design framework for stable, bright, and tunable scintillation platforms with applications in precision health, space exploration and hard X-ray imaging at next-generation free-electron
laser facilities.
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All authors Parivash Moradifar, Tim Brandt van Driel, Masashi Fukuhara, Cindy Shi, Ariel Stiber, Federico Moretti, Qingyuan Fan, Diana Jeong, Aaron M. Lindenberg, Garry Chinn, Craig S. Levin, Jennifer A. Dionne
Biological Physics
Influence of Plaque Characteristics on Stent Biomechanical Outcomes - A Case Study on Double Kissing Crush Coronary Stenting
Andrea Colombo, Dario Carbonarob, Mingzi Zhang et al. (+5 more)
Background Double Kissing (DK) Crush is a two-stent technique for complex coronary bifurcation lesions, yet the biomechanical influence of plaque on its performance remains poorly understood. This study developed a computational biomechanical model of the DK-Crush procedure to quantify how plaque presence and composition affect procedural outcomes and the performance of Xience Sierra and Orsiro stents. Methods A population-representative coronary bifurcation was modelled with no plaque, lipid plaque, and fibrous plaque. The complete DK-Crush sequence was simulated using finite element analysis for both stent platforms. Mechanical outcomes included arterial wall stress, malapposition, side branch ostium clearance, and residual stenosis. Post-deployment hemodynamics was assessed using pulsatile computational fluid dynamics, quantifying high shear rate volume and lumen area exposed to low time-averaged endothelial shear stress (TAESS). Results Plaque presence and stiffness reduced lumen restoration, increased arterial wall stress, led to larger high shear rate regions and, for fibrous plaque, increased exposure to low TAESS. Malapposition and ostial clearance depended mainly on stent design. Plaque also altered the relative performance of the two platforms, revealing differences not observed in plaque-free models. Conclusions Plaque characteristics substantially affect DK-Crush biomechanics and modify stent behaviour. Incorporating plaque is therefore essential for realistic computational evaluation of bifurcation stenting.
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All authors Andrea Colombo, Dario Carbonarob, Mingzi Zhang, Chi Shen, Ankush Kapoor, Nigel Jepson, Claudio Chiastra, Susann Beier
Optics · Cross-listed from quant-ph
Ghost imaging with zero photons
Meixue Chen, Yiqi Song, Yu Gu et al. (+8 more)
Ghost imaging was first demonstrated with entangled
photon pairs and well-known for its peculiar properties. The signal beam that illuminates the object possesses no spatial resolution, whereas the reference beam, which never interacts with the object, is spatially resolved. Either beam alone cannot retrieve the image, which can only be obtained when the signal and reference beams are correlated. Here we will report a ghost imaging experiment with even more peculiar properties, in which the image can be reconstructed when no
photon interacts with the object or even no
photon in neither signal nor reference beam. All the
photons interacted with the object are discarded. Only the time bins with zero
photon are employed to retrieve the image, a process referred to as "ghost imaging with zero
photons" hereafter. The reason why ghost image can be retrieved with zero
photons is jointly determined by
photon-number projection measurement and
photon statistics of thermal light. The results are helpful to resolve the debate on the physics of ghost imaging and understand the relation between
quantum and classical correlations.
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All authors Meixue Chen, Yiqi Song, Yu Gu, Huafan Zhang, Huaibin Zheng, Yuchen He, Hui Chen, Yu Zhou, Fuli Li, Zhuo Xu, Jianbin Liu
Biological Physics
Biogenic bubbles enable microbial escape from physical confinement
Babak Vajdi Hokmabad, Thomas Appleford, Hao Nghi Luu et al. (+3 more)
Immotile microbes inhabit nearly every environment on Earth, from soils and sediments to food
matrices – yet how they disperse through these physically confining environments is poorly understood. Here, we show that immotile microbial colonies confined in a model transparent yield-stress
matrix can achieve long-range dispersal by harnessing their own metabolism. Using yeast as a model organism, we find that fermentation drives dissolved CO$_2$ to supersaturation, nucleating biogenic bubbles that grow, yield the
matrix, and rise, hydrodynamically entraining
cells vertically in their wake. Sequential bubble nucleation sculpts persistent columnar colonies extending far beyond what growth alone permits. Multiple colonies interact via their fermentation byproducts, merging and mixing genetically as they collectively sculpt self-sustaining conduit networks. Our findings reveal a third mode of microbial dispersal, distinct from the canonical mechanisms of motility and growth, with implications for ecology, environmental science, and biotechnology. More broadly, they exemplify a previously unrecognized class of active behavior – Metabolically Driven Active Matter – in which metabolic byproducts reshape the physical landscape of confinement to drive population-scale motion.
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All authors Babak Vajdi Hokmabad, Thomas Appleford, Hao Nghi Luu, Meera Ramaswamy, Maziyar Jalaal, Sujit S. Datta
Fluid Dynamics
Evidence of an inertialess Kapitza instability due to viscosity stratification
Shravya Gundavarapu, Darish Jeswin Dhas and Anubhab Roy
The classical Kapitza instability of a
gravity-driven falling film requires finite inertia to operate. We show that a surface-mode instability can arise in the complete absence of inertia when the film possesses a continuous viscosity stratification, a feature relevant to particle-laden films with shear-induced migration, thermally stratified coatings, and concentration-graded flows. The viscosity field, prescribed as a linear profile across the film thickness, evolves through an advection-
diffusion equation characterized by a P$\'{e}$clet number. Using long-wave asymptotics and Chebyshev spectral computations, we solve the coupled
eigenvalue problem for the perturbation streamfunction and viscosity fields and demonstrate that viscosity stratification destabilizes the surface mode in the zero-inertia (Stokes) limit. The instability is confined to a finite window of P$\'{e}$clet numbers. Increasing the stratification parameter lowers the critical P$\'{e}$clet number, broadens the range of unstable wavenumbers, and increases the growth rate. The instability mechanism is traced to the phase relationship between perturbation vorticity and the interface displacement: viscosity stratification shifts the vorticity to a lagging configuration, which reinforces interface deformation, following the framework of Hinch (1984). The mechanism bears a structural resemblance to the surfactant-driven Marangoni instability in creeping two-layer flows, extending this class of scalar-mediated, inertialess instabilities to bulk viscosity stratification.
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Applied Physics · Cross-listed from cs.DC
Reduced-Mass Orbital AI Inference via Integrated Solar, Compute, and Radiator Panels
Stephen Gaalema, Samuel Indyk and Clinton Staley
We describe and analyze a distributed compute architecture for SSO computational satellites that can potentially provide >100 kW compute power per launched metric ton (including deployment and station keeping mass). The architecture co-locates and integrates the
solar cells, radiator, and compute functions into multiple small panels arranged in a large array. The resultant large vapor chamber radiator area per panel should permit ICs to operate at junction temperatures near 40*C with benefits in compute efficiency and reliability. Using the structure of the radiator to support the
solar cells may also yield a specific power of about 500 W/kg compared to less than 100 for existing conventional implementations. Assuming development of custom solutions for all components, a 16 MW computation, 150 ton satellite comprising a 20 m x 2200 m grid of 16,000 panels can fit in a single Starship hold. The concept is scalable to much larger satellites with higher mass payloads or using on-orbit assembly. We consider panel sizes from 1 to 4 m2 to allow trading vapor chamber heat transport with compute efficiency and inter-panel communication. Assuming a 1 kW/panel design, 512-panel subarrays of the satellite can run a representative inference-only LLM with 500,000
token context window and 128
attention blocks, at a rate of 553
tokens/sec/session, across 256 simultaneous in-flight sessions. A full satellite could support 31 such subarrays, for >7900 inferences at a time.
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Computational Physics · Cross-listed from cs.LG
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU)
Jingye Tan, Govinda Anantha Padmanabha, Steven J. Yang et al. (+1 more)
Recent progress in AI-enabled constitutive modeling has concentrated on moving from a purely data-driven paradigm to the enforcement of physical constraints and mechanistic principles, a concept referred to as physics augmentation. Classical phenomenological approaches rely on selecting a pre-defined model and calibrating its parameters, while machine learning methods often focus on discovery of the model itself. Sparse regression approaches lie in between, where large libraries of pre-defined models are probed during calibration. Sparsification in the aforementioned paradigm, but also in the context of neural network architecture, has been shown to enable interpretability, uncertainty quantification, but also heterogeneous software integration due to the low-dimensional nature of the resulting models. Most works in AI-enabled constitutive modeling have also focused on data from a single source, but in reality, materials modeling workflows can contain data from many different sources (multi-modal data), and also from testing other materials within the same materials class (multi-fidelity data). In this work, we introduce physics augmented finite element model updating (paFEMU), as a transfer learning approach that combines AI-enabled constitutive modeling, sparsification for interpretable model discovery, and finite element-based adjoint optimization utilizing multi-modal data. This is achieved by combining simple mechanical testing data, potentially from a distinct material, with digital image correlation-type full-field data acquisition to ultimately enable rapid constitutive modeling discovery. The simplicity of the sparse representation enables easy integration of neural constitutive models in existing finite element workflows, and also enables low-dimensional updating during transfer learning.
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All authors Jingye Tan, Govinda Anantha Padmanabha, Steven J. Yang, Nikolaos Bouklas
Fluid Dynamics
Quantifying Injection-Driven Interphase Mass Transfer within Porous Media via Time-Elapsed X-ray micro-Computed Tomography
Christopher A. Allison, Ruotong Huang, Anindityo Patmonoaji et al. (+2 more)
Understanding interphase mass transfer is essential for a variety of applications in porous media, from groundwater remediation to geologic energy storage. While X-ray micro-Computed
Tomography (micro-CT) is a measurement technique that can provide critical in situ observations, quantifying transient mass transfer requires models and workflows capable of compensating for the spatial and temporal resolution constraints. Current literature presents three analytical frameworks for evaluating interphase mass transfer using time-lapsed sequences of X-ray micro-CT scans: the Slice-Averaged Concentration approach, the Non-Classified per-Cluster approach, and the Classified per-Cluster approach. We systematically evaluate the frameworks and results for each of the three approaches as applied to several sequences of time-lapsed scans, each observing hydrogen dissolution experiments at varying injection rates. Estimates of average mass transfer coefficients vary within an order of magnitude, increasing as injection rate decreases. However, approach estimates diverged when approximating more complex, pore-scale phenomena, such as aqueous solute concentration. Per-Cluster approaches preserve more information, enabling quantification of important localized physicochemical processes such as dissolution fingering and cluster remobilization. Given the lack of ground-truth
benchmarks for mass-transfer kinetic data in the conditions of interest to fields such as geologic storage, additional research is still needed to understand how these approaches vary under different state and flow conditions. The utility of one approach over another is ultimately determined by the desired level of system detail, at the cost of the computational resources required to achieve it. Our results provide a framework for researchers to select analytical approaches based on the desired level of physical detail.
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All authors Christopher A. Allison, Ruotong Huang, Anindityo Patmonoaji, Lydia Knuefing, Anna L. Herring
Optics
Controllable Chirality Sorting of Particles via Topological Optical Quasiparticles
Hao Zhang, Xi Xie and Yijie Shen
The manipulation and sorting of
chiral nanoparticles are of fundamental importance in multidisciplinary fields ranging from biochemistry to nanophotonics. In this study, we propose a novel and controllable chirality sorting mechanism for continuous particle separation using focused topological optical quasiparticles. Specifically, we investigate the sorting dynamics driven by tight-focused optical skyrmions and bimerons consisting of tailored spatial modes. By highly focusing free-space topological structure light fields, we generate intricate non-paraxial focal fields with tailored intensity and topological polarization textures. The sorting dynamics are systematically evaluated under the dipole approximation for fused silica nanoparticles. Our analytical calculation demonstrate that optical forces exert opposite directional pushes on particles of opposite chiralities, enabling highly efficient spatial separation. Notably, we demonstrate that this sorting process is controllable; by tuning the topological charges, the sorting distance can be flexibly tailored and expanded. The dynamic sorting process in customized topological structures introduces a promising new paradigm for tunable, wide-range chirality sorting of micro- and nano-particles.
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Physics and Society
Modeling non-Poissonian temporal hypergraphs by Markovian node dynamics
Hang-Hyun Jo and Naoki Masuda
Temporal hypergraphs capture time-resolved group interactions among nodes. Empirical data support that time-stamped group interactions show bursty event sequences and non-trivial temporal correlations. In the present study, we introduce node-driven temporal hypergraph models in which each node stochastically alternates between low- and high-activity states, and a hyperedge produces time-stamped events with a probability that depends on the number of high-state nodes in the hyperedge. For two event-generation rules, we analytically derive interevent time distributions and autocorrelation functions of event sequences, both for hyperedges and nodes. Despite Markovian node-state dynamics, the induced event processes become mixtures of Poissonian, short-tailed components, resulting in longer-tailed interevent time distributions and slowly decaying autocorrelation. The theory further shows the dependence of these features on the size of hyperedge, which largely agrees with various empirical data. We expect our models to provide a simple, interpretable framework for connecting individual-level activity fluctuations to the timing patterns observed in real group interactions.
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Optics
A thermoelastic limit on the focal intensity in Fabry-Pérot cavities
Jeremy J. Axelrod, Lothar Maisenbacher, Ashwin Singh et al. (+4 more)
Light in the mode of a Fabry-Pérot cavity heats the mirror surfaces via optical absorption, causing thermoelastic deformation of the mirror substrates, which in turn dictates the shape of the mode. We develop an analytical model which predicts that this effect limits the maximum focal intensity of the mode. Using two near-concentric Fabry-Pérot cavities – one with 4.5-fold higher mirror absorption than the other – we measure the thermoelastic properties of the cavity mirrors and demonstrate that it is possible to achieve at least 70% of this predicted limit (in the high-absorption cavity), and that the predicted limit is 2.9 TW/cm^2 (in the low-absorption cavity).
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All authors Jeremy J. Axelrod, Lothar Maisenbacher, Ashwin Singh, Isaac M. Pope, Petar N. Petrov, Jessie T. Zhang, Holger Müller
Applied Physics
High Performance 4H-SiC Optically Controlled MOS Transistor
Sitian Chen, Ziqian Tian, Guoliang Zhang et al. (+7 more)
This paper introduces an optically controlled 4H-SiC MOSFET designed to avoid the gate-
oxide interface unreliability and electromagnetic interference (EMI) susceptibility inherent in conventional voltage-driven devices. By replacing the conventional gate electrode with a semi-transparent optical window, the device enables direct modulation of channel conductivity through ultraviolet illumination. Electrical and optical characterization demonstrates that under an optical power density above 0.1 W/cm^2, the device achieves an on/off current ratio exceeding 10^6 between illuminated and dark states. Notably, at an optical power density of 0.031 W/cm^2, the photogenerated current density exceeds that obtained under a gate bias of 15 V in magnitude. Energy band analysis confirms that the optical switching mechanism operates through direct photogenerated carrier generation and transport, fundamentally differing from conventional gate voltage control and thus circumventing interface-trap and EMI-related limitations. Dynamic measurements further reveal fast switching capability, with a rise time of 1.44 ns. These results validate the feasibility of optically driven switching in SiC-based devices and highlight their potential for high-speed logic applications.
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All authors Sitian Chen, Ziqian Tian, Guoliang Zhang, Jiafa Cai, Rongdun Hong, Xiaping Chen, Dingqu Lin, Shaoxiong Wu, Yuning Zhang, Feng Zhang
Optics
Reconfigurable Momentum-space vectorial lasing enabled by Quasi-BIC
Hongyu Yuan, Zimeng Zeng, Jiayao Liu et al. (+4 more)
Bound states in the continuum (BICs) have enabled
lasers with rich momentum-space textures. However, the output patterns of quasi-BIC
lasers remain largely static and confined to a few geometries. Here, a reconfigurable momentum-space vectorial
laser was proposed based on two-dimensional photonic
crystal. By selectively exciting quasi-BIC modes, we identify the
geometric asymmetry factors favoring single BIC, dual-BIC, and radiative mode with BIC operation. This approach yields vectorial lasing with characteristic patterns lasing in momentum space of bidirectional double lobes (BDL), radially polarized ring with BDL, azimuthally polarized ring with BDL, and linearly polarized spot with BDL. Importantly, reversible switching between a single donut and a donut with BDL was achieved in the same device by varying the pump energy density. Our work establishes a compact, versatile platform for reconfigurable vectorial
lasers, with potential applications in tunable optical tweezers, super-resolution imaging, and on-chip optical interconnects.
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All authors Hongyu Yuan, Zimeng Zeng, Jiayao Liu, Zhuoyang Li, Xiaolin Wang, Zelong He, Zhaona Wang